Publications

Our Publications

Information about development of CytoSolve™ has evinced keen interest among the research community world-wide. A number of papers have been written on the CytoSolve™ platform and its components. Researchers in the fields of computational biology and development of drug and treatment plans are using CytoSolve™ to build their thesis.

You can view and read papers published on CytoSolve™ and papers published by other researchers that cite CytoSolve™ on this page.

Papers

In-Silico Analysis & In-Vivo Results Concur on Glutathione Depletion in Glyphosate Resistant GMO Soy, Advancing a Systems Biology Framework for Safety Assessment of GMOs
This study advances previous efforts towards development of computational systems biology, in silico, methods for biosafety assessment of genetically modified organisms (GMOs). C1 metabolism is a critical molecular system in plants, fungi, and bacteria. In our previous research, critical molecular systems of C1 metabolism were identified and modeled using CytoSolve?, a platform for in silico analysis. In addition, multiple exogenous molecular systems affecting C1 metabolism such as oxidative stress, shikimic acid metabolism, glutathione biosynthesis, etc. were identified. Subsequent research expanded the C1 metabolism computational models to integrate oxidative stress, suggesting glutathione (GSH) depletion. Recent integration of data from the EPSPS genetic modification of Soy, also known as Roundup Ready Soy (RRS), with C1 metabolism predicts similar GSH depletion and HCHO accumulation in RRS. The research herein incorporates molecular systems of glutathione biosynthesis and glyphosate catabolism to expand the extant in silico models of C1 metabolism. The in silico results predict that Organic Soy will have a nearly 250% greater ratio of GSH and GSSG, a measure of glutathione levels, than in RRS that are glyphosate-treated glyphosate-resistant Soy versus the Organic Soy. These predictions also concur with in vivo greenhouse results. This concurrence suggests that these in silico models of C1 metabolism may provide a viable and validated platform for biosafety assessment of GMOs, and aid in selecting rational criteria for informing in vitro and in vivo efforts to more accurately decide in the problem formulation phase whose parameters need to be assessed so that conclusion on “substantial equivalence” or material difference of a GMO and its non-GMO counterpart can be drawn on a well-grounded basis.

Do GMOs Accumulate Formaldehyde and Disrupt Molecular Systems Equilibria? Systems Biology May Provide Answers
Safety assessment of genetically modified organisms (GMOs) is a contentious topic. Proponents of GMOs assert that GMOs are safe since the FDA’s policy of substantial equivalence considers GMOs “equivalent” to their non-GMO counterparts, and argue that genetic modification (GM) is simply an extension of a “natural” process of plant breeding, a form of “genetic modification”, though done over longer time scales. Anti-GMO activists counter that GMOs are unsafe since substantial equivalence is unscientific and outdated since it originates in the 1970s to assess safety of medical devices, which are not comparable to the complexity of biological systems, and contend that targeted GM is not plant breeding. The heart of the debate appears to be on the methodology used to determine criteria for substantial equivalence. Systems biology, which aims to understand complexity of the whole organism, as a system, rather than just studying its parts in a reductionist manner, may provide a framework to determine appropriate criteria, as it recognizes that GM, small or large, may affect emergent properties of the whole system. Herein, a promising computational systems biology method couples known perturbations on five biomolecules caused by the CP4 EPSPS GM of Glycine max L. (soybean), with an integrative model of C1 metabolism and oxidative stress (two molecular systems critical to plant function). The results predict significant accumulation of formaldehyde and concomitant depletion of glutathione in the GMO, suggesting how a “small” and single GM creates “large” and systemic perturbations to molecular systems equilibria. Regulatory agencies, currently reviewing rules for GMO safety, may wish to adopt a systems biology approach using a combination of in silico, computational methods used herein, and subsequent targeted experimental in vitro and in vivo designs, to develop a systems understanding of “equivalence” using biomarkers, such as formaldehyde and glutathione, which predict metabolic disruptions, towards modernizing the safety assessment of GMOs.

Services-Based Systems Architecture for Modeling the Whole Cell: A Distributed Collaborative Engineering Systems Approach
Modeling the whole cell is a goal of modern systems biology. Current approaches are neither scalable nor flexible to model complex cellular functions. They do not support collaborative development, are monolithic and, take a primarily manual approach of combining each biological pathway model’s software source code to build one large monolithic model that executes on a single computer. What is needed is a distributed collaborative engineering systems approach that offers massive scalability and flexibility, treating each part as a services-based component, potentially delivered by multiple suppliers, that can be dynamically integrated in real-time. A requirements specification for such a services-based architecture is presented. This specification is used to develop CytoSolve, a working prototype that implements the services-based architecture enabling dynamic and collaborative integration of an ensemble of biological pathway models, that may be developed and maintained by teams distributed globally. This architecture computes solutions in a parallel manner while offering ease of maintenance of the integrated model. The individual biological pathway models can be represented in SBML, CellML or in any number of formats. The EGFR model of Kholodenko with known solutions is first tested within the CytoSolve framework to prove it viability. Success of the EGFR test is followed with the development of an integrative model of interferon (IFN) response to virus infection using the CytoSolve platform. The resulting integrated model of IFN yields accurate results based on comparison with previously published in vitro and in vivo studies. A open web-based environment for collaborative testing and continued development is now underway and available on www.cytosolve.com. As more biological pathway models develop in a disparate and decentralized manner, this architecture offers a unique platform for collaborative systems biology, to build large-scale integrative models of cellular function, and eventually one day model the whole cell.

Multiscale Mathematical Modeling to Support Drug Development
It is widely recognized that major improvements are required in the methods currently being used to develop new therapeutic drugs. The time from initial target identification to commercialization can be 10–14 years and incur a cost in the hundreds of millions of dollars. Even after substantial investment, only 30–40% of the candidate compounds entering clinical trials are successful. We propose that multiscale mathematical pathway modeling can be used to decrease time required to bring candidate drugs to clinical trial and increase the probability that they will be successful in humans. The requirements for multiple time scales and spatial scales are discussed, and new computational paradigms are identified to address the increased complexity of modeling.

Pericytes of the neurovascular unit: key functions and signaling pathways
Pericytes are vascular mural cells embedded in the basement membrane of blood microvessels. They extend their processes along capillaries, pre-capillary arterioles and post-capillary venules. CNS pericytes are uniquely positioned in the neurovascular unit between endothelial cells, astrocytes and neurons. They integrate, coordinate and process signals from their neighboring cells to generate diverse functional responses that are critical for CNS functions in health and disease, including regulation of the blood–brain barrier permeability, angiogenesis, clearance of toxic metabolites, capillary hemodynamic responses, neuroinflammation and stem cell activity. Here we examine the key signaling pathways between pericytes and their neighboring endothelial cells, astrocytes and neurons that control neurovascular functions. We also review the role of pericytes in CNS disorders including rare monogenic diseases and complex neurological disorders such as Alzheimer’s disease and brain tumors. Finally, we discuss directions for future studies.

In Silico Modeling of Shear-Stress-Induced Nitric Oxide Production in Endothelial Cells through Systems Biology
Nitric oxide (NO) produced by vascular endothelial cells is a potent vasodilator and an antiinflammatory mediator. Regulating production of endothelial-derived NO is a complex undertaking, involving multiple signaling and genetic pathways that are activated by diverse humoral and biomechanical stimuli. To gain a thorough understanding of the rich diversity of responses observed experimentally, it is necessary to account for an ensemble of these pathways acting simultaneously. In this article, we have assembled four quantitative molecular pathways previously proposed for shear-stress-induced NO production. In these pathways, endothelial NO synthase is activated 1), via calcium release, 2), via phosphorylation reactions, and 3), via enhanced protein expression. To these activation pathways, we have added a fourth, a pathway describing actual NO production from endothelial NO synthase and its various protein partners. These pathways were combined and simulated using CytoSolve, a computational environment for combining independent pathway calculations. The integrated model is able to describe the experimentally observed change in NO production with time after the application of fluid shear stress. This model can also be used to predict the specific effects on the system after interventional pharmacological or genetic changes. Importantly, this model reflects the up-to-date understanding of the NO system, providing a platform upon which information can be aggregated in an additive way.

A Distributed Computational Architecture for Integrating Multiple Biomolecular Pathways
Biomolecular pathways are building blocks of cellular biochemical function. Computational biology is in rapid transition from diagrammatic representation of pathways to quantitative and predictive mathematical models, which span time-scales, knowledge domains and spatial-scales. This transition is being accelerated by high-throughput experimentation which isolates reactions and their corresponding rate constants.

Integrating an Ensemble of Distributed Biochemical Network Models
A new system for integrating an ensemble of distributed biochemical network models is presented. Rapid growth in the number of biochemical network models, created in different formats, across different computing systems, with minimal input and output information, necessitates the need for such a system in order to build large scale models in a flexible and scalable manner.

The development of a fully-integrated immune response model (FIRM) simulator of the immune response through integration of multiple subset models
The complexity and multiscale nature of the mammalian immune response provides an excellent test bed for the potential of mathematical modeling and simulation to facilitate mechanistic understanding. Historically, mathematical models of the immune response focused on subsets of the immune system and/or specific aspects of the response. Mathematical models have been developed for the humoral side of the immune response, or for the cellular side, or for cytokine kinetics, but rarely have they been proposed to encompass the overall system complexity. We propose here a framework for integration of subset models, based on a system biology approach.

V. A. SHIVA AYYADURAI, PRABHAKAR DEONIKAR

Safety assessment of genetically modified organisms (GMOs) is a contentious topic. Proponents of GMOs assert that GMOs are safe since the FDA’s policy of substantial equivalence considers GMOs “equivalent” to their non-GMO counterparts, and argue that genetic modification (GM) is simply an extension of a “natural” process of plant breeding, a form of “genetic modification”, though done over longer time scales. Anti-GMO activists counter that GMOs are unsafe since substantial equivalence is unscientific and outdated since it originates in the 1970s to assess safety of medical devices, which are not comparable to the complexity of biological systems, and contend that targeted GM is not plant breeding. The heart of the debate appears to be on the methodology used to determine criteria for substantial equivalence. Systems biology, which aims to understand complexity of the whole organism, as a system, rather than just studying its parts in a reductionist manner, may provide a framework to determine appropriate criteria, as it recognizes that GM, small or large, may affect emergent properties of the whole system. Herein, a promising computational systems biology method couples known perturbations on five biomolecules caused by the CP4 EPSPS GM of Glycine max L. (soybean), with an integrative model of C1 metabolism and oxidative stress (two molecular systems critical to plant function). The results predict significant accumulation of formaldehyde and concomitant depletion of glutathione in the GMO, suggesting how a “small” and single GM creates “large” and systemic perturbations to molecular systems equilibria. Regulatory agencies, currently reviewing rules for GMO safety, may wish to adopt a systems biology approach using a combination of in silico, computational methods used herein, and subsequent targeted experimental in vitro and in vivo designs, to develop a systems understanding of “equivalence” using biomarkers, such as formaldehyde and glutathione, which predict metabolic disruptions, towards modernizing the safety assessment of GMOs.

It is widely recognized that major improvements are required in the methods currently being used to develop new therapeutic drugs. The time frominitial target identification to commercialization can be 10–14 years and incur a cost in the hundreds of millions of dollars. Even after substantial investment, only 30–40% of the candidate compounds entering clinical trials are successful. We propose that multiscale mathematical pathway modeling can be used to decrease time required to bring candidate drugs to clinical trial and increase the probability that they will be successful in humans. The requirements for multiple time scales and spatial scales are discussed, and new computational paradigms are identified to address the increased complexity of modeling.

Pericytes of the neurovascular unit: key functions and signaling pathways

MELANIE D SWEENEY, SHIVA AYYADURAI & BERISLAV V ZLOKOVIC

Pericytes are vascular mural cells embedded in the basement membrane of blood microvessels. They extend their processes along capillaries, pre-capillary arterioles and post-capillary venules. CNS pericytes are uniquely positioned in the neurovascular unit between endothelial cells, astrocytes and neurons. They integrate, coordinate and process signals from their neighboring cells to generate diverse functional responses that are critical for CNS functions in health and disease, including regulation of the blood–brain barrier permeability, angiogenesis, clearance of toxic metabolites, capillary hemodynamic responses, neuroinflammation and stem cell activity. Here we examine the key signaling pathways between pericytes and their neighboring endothelial cells, astrocytes and neurons that control neurovascular functions. We also review the role of pericytes in CNS disorders including rare monogenic diseases and complex neurological disorders such as Alzheimer’s disease and brain tumors. Finally, we discuss directions for future studies.

Combinatorial drug therapy for cancer in the post-genomic era

BISSAN AL-LAZIKANI, UDAI BANERJI & PAUL WORKMAN

Over the past decade, whole genome sequencing and other ‘omics’ technologies have defined pathogenic driver mutations to which tumor cells are addicted. Such addictions, synthetic lethalities and other tumor vulnerabilities have yielded novel targets for a new generation of cancer drugs to treat discrete, genetically defined patient subgroups. This personalized cancer medicine strategy could eventually replace the conventional one-size-fits-all cytotoxic chemotherapy approach. However, the extraordinary intratumor genetic heterogeneity in cancers revealed by deep sequencing explains why de novo and acquired resistance arise with molecularly targeted drugs and cytotoxic chemotherapy, limiting their utility. One solution to the enduring challenge of polygenic cancer drug resistance is rational combinatorial targeted therapy.

In Silico Modeling of Shear-Stress-Induced Nitric Oxide Production in Endothelial Cells through Systems Biology

Nitric oxide (NO) produced by vascular endothelial cells is a potent vasodilator and an antiinflammatory mediator. Regulating production of endothelial-derived NO is a complex undertaking, involving multiple signaling and genetic pathways that are activated by diverse humoral and biomechanical stimuli. To gain a thorough understanding of the rich diversity of responses observed experimentally, it is necessary to account for an ensemble of these pathways acting simultaneously. In this article, we have assembled four quantitative molecular pathways previously proposed for shear-stress-induced NO production. In these pathways, endothelial NO synthase is activated 1), via calcium release, 2), via phosphorylation reactions, and 3), via enhanced protein expression. To these activation pathways, we have added a fourth, a pathway describing actual NO production from endothelial NO synthase and its various protein partners. These pathways were combined and simulated using CytoSolve, a computational environment for combining independent pathway calculations. The integrated model is able to describe the experimentally observed change in NO production with time after the application of fluid shear stress. This model can also be used to predict the specific effects on the system after interventional pharmacological or genetic changes. Importantly, this model reflects the up-to-date understanding of the NO system, providing a platform upon which information can be aggregated in an additive way.

V. A. S. AYYADURAI

Modeling the whole cell is a goal of modern systems biology. Current approaches are neither scalable nor flexible to model complex cellular functions. They do not support collaborative development, are monolithic and, take a primarily manual approach of combining each biological pathway model’s software source code to build one large monolithic model that executes on a single computer. What is needed is a distributed collaborative engineering systems approach that offers massive scalability and flexibility, treating each part as a services-based component, potentially delivered by multiple suppliers, that can be dynamically integrated in real-time. A requirements specification for such a services-based architecture is presented. This specification is used to develop CytoSolve, a working prototype that implements the services-based architecture enabling dynamic and collaborative integration of an ensemble of biological pathway models, that may be developed and maintained by teams distributed globally. This architecture computes solutions in a parallel manner while offering ease of maintenance of the integrated model. The individual biological pathway models can be represented in SBML, CellML or in any number of formats. The EGFR model of Kholodenko with known solutions is first tested within the CytoSolve framework to prove it viability. Success of the EGFR test is followed with the development of an integrative model of interferon (IFN) response to virus infection using the CytoSolve platform. The resulting integrated model of IFN yields accurate results based on comparison with previously published in vitro and in vivo studies. A open web-based environment for collaborative testing and continued development is now underway and available on www.cytosolve.com. As more biological pathway models develop in a disparate and decentralized manner, this architecture offers a unique platform for collaborative systems biology, to build large-scale integrative models of cellular function, and eventually one day model the whole cell.

V. A. SHIVA AYYADURAI and C. FORBES DEWEY JR

A grand challenge of computational systems biology is to create a molecular pathway model of the whole cell. Current approaches involve merging smaller molecular pathway models’ source codes to create a large monolithic model (computer program) that runs on a single computer. Such a larger model is difficult, if not impossible, to maintain given ongoing updates to the source codes of the smaller models. This paper describes a new system called CytoSolve that dynamically integrates computations of smaller models that can run in parallel across different machines without the need to merge the source codes of the individual models. This approach is demonstrated on the classic Epidermal Growth Factor Receptor (EGFR) model of Kholodenko. The EGFR model is split into four smaller models and each smaller model is distributed on a different machine. Results from four smaller models are dynamically integrated to generate identical results to the monolithic EGFR model running on a single machine. The overhead for parallel and dynamic computation is approximately twice that of a monolithic model running on a single machine. The CytoSolve approach provides a scalable method since smaller models may reside on any computer worldwide, where the source code of each model can be independently maintained and updated.